Presentation on theme: "John M. Connor Purdue University, West Lafayette, Indiana Douglas J. Miller University of Missouri, Columbia, Missouri"— Presentation transcript:
John M. Connor Purdue University, West Lafayette, Indiana Douglas J. Miller University of Missouri, Columbia, Missouri
Objective Primary: To estimate the determinants of fines imposed on companies by the EC for hard core global price-fixing. To test whether the optimal-deterrence theory of crime has predictive validity. To gather other evidence of the effectiveness of the EUs anti-cartel enforcement.
Motivations DG-COMP is an exemplar for many newer antitrust authorities After 40 years, time for a retrospective economic analysis of EC enforcement Critics of the ECs cartel fining practices: 1. Assert that sentencing is idiosyncratic 2. Question predictability, transparency, and proportionality.
Rates of Discovery by the European Commission Rising 1/1/20144J M Connor, Purdue U.
Rates of Discovery of Global Cartels Peaked in Global cartels affect prices in two or more continents 1/1/20145J M Connor, Purdue U.
Optimal Deterrence Model The first order conditions for an optimally deterring EC fine, ECF* is: ECF* = (HARM/p) – OTHPEN, where HARM is the antitrust injuries caused, p is the probability of detection & conviction, OTHPEN is all other penalties known or expected at the time of the EC decision.
Previous Studies Cohen (1996) first to examine econometrically the size of U.S. corporate criminal penalties (median $10 K ); sample of 961 companies during , but only 8% antitrust; a subsample of 285 observations has an estimate of harm, but none were antitrust decisions. Connor and Miller (2009) analyze 108 U.S. DOJ corporate fines for global price fixing for the years 1995 to This study is the immediate antecedent of the present paper: same data source, similar methods, and tests the same hypotheses suggested by optimal deterrence theory. Interesting comparative findings.
Data Source: the PICs Set Comprises 192 companies fined for cartel violations 1/1990 to 1/2009. Mean affected sales US $3.2 billion in EU All global cartels, mean global sales US $7.9 billion Mean EC fine US $33 million, but highly skewed 8% were big riggers 41% in chemicals, 26% in ocean shipping conferences 6 received extra penalties for EU recidivism, but 73% became recidivists somewhere by 12/2008
Augmented Behavioral Model Ln(ECF) = α + βLn(HARM) + γ(1/p) + δ 1 OTHPEN + δ 2 OTHPEN 2 + λCONTROLS + ε. Proxy for HARM is affected sales in EU. AS EU is positively correlated with HARM EU. 1/p is a vector of 8 proxies for detection & conviction. OTHPEN is non-EC fines and settlements. A vector of control variables for time trends, new EC fining policies, firms HQ,, and 7 industries.
Hypotheses Optimal deterrence principles imply: 1. HARM should be positively related to penalties 2. Factors that raise p are negative (e.g., AMNESTY, LENIENCY 1 & 2 ), and vice-versa (N, BIDRIG, etc.) 3. OTHPEN ought to be negative Controls include trend (+), Monti (+/-), Kroes (+/-), GUIDELINES 1 & 2 (+), DURATION (+), CAP (-), NO AM and ASIA (+/-), and 7 industry dummies.
Methods and Adjustments We had missing data (recoded as zero) for a few variables. After initially estimating the model, Ramsey's RESET procedure found evidence of specification errors. Because 7% of ECF censored, ran OLS and ML TOBIT All monetary values were highly skewed, so we transformed ECF, HARM to natural logs. Dropped 13 variables due to very low significance Whites Test did not reject homoskedasticity
Estimation Results 1 OLS and ML Tobit have similar fit, signs, and significance of coefficients. OLS R 2 = 68.1% is very satisfactory. Fit is three times better than Cohen study and about the same as our US fine regressions.
Estimation Results 2 Elasticity of ECF wrt HARM is ε = Optimal deterrence ex post requires ε = 1.0 By comparison: 1. Cohen (1996) estimates ε = Connor and Miller (2009) estimate ε =
Estimation Results 3 All five detection-related variables have correct signs: 1. BIDRIG raises ECF by 87%, ceteris paribus 2. EU RECID typically raises fines 135% per prior instance 3. Non-EU recidivism typically raises fines 18% (Above 3 highly significant, 2 below not quite) 1. AMNESTY 2 lowered fines for non-amnestied firms 20% 2. No. of cartelists N has a negative sign OTHPEN describes an upward-bending parabola, negative only if above $600 million (like US)
Estimation Results 4 TIME adds 8.4% per year to expected ECF. Highly significant, GUIDELINES 2 adds 107%. MONTI regime was 110% below trend. KROES regime (with TIME > 15 years) is on trend. Van Miert and earlier Commissioners is reference group. North American cartelists pay 62% more than EU firms. The 5 remaining industry dummies are strongly positive, with METALS, ORGCHEM, GRAPHITE especially large. Reference group is shipping and misc. manufacturing. Chow test shows shipping fines same as rest of sample.
Policy Discussion Optimal deterrence model predicts well for global cartel fines by US DOJ and Eur. Commission. Emerging convergence: ε = +0.3 to +0.4 (suboptimal) EC ignores non-EU penalties, unless VERY large Recidivism anywhere increases severity of ECFs Time controls show EC becoming more aggressive* Firms in cartel-prone industries will be hit hard* Bid rigging is an aggravating factor* Does NO AM result challenge proportionality?* * Unmentioned in EC Fining Guidelines
Sources Connor, John M. Cartels and Antitrust Portrayed: Detection: SSRN Working Paper (March 2009). [http://ssrn.com/abstract= ]http://ssrn.com/abstract= Connor, John M. and Douglas J. Miller. Determinants of EC Antitrust Fines for Members of Global Cartels, paper at the 3 rd Conference on The Economics of Competition Law, sponsored by LEAR, Rome, June 25-26, Connor, John M. and Douglas J. Miller. Determinants of U.S. Antitrust Fines of Corporate Participants of Global Cartels, paper presented at the 7 th International Industrial Organization Conference, Boston, April 3-5, Cohen, Mark A. Theories of Punishment and Empirical Trends in Corporate Criminal Sanctions. Managerial and Decision Economics 17 (1996):